A cardinal dissensus measure based on the Mahalanobis distance

Producción CientíficaIn this paper we address the problem of measuring the degree of consensus/dissensus in a context where experts or agents express their opinions on alternatives or issues by means of cardinal evaluations. To this end we propose a new class of distance-based consensus model, the family of the Mahalanobis dissensus measures for profiles of cardinal values. We set forth some meaningful properties of the Mahalanobis dissensus measures. Finally, an application over a real empirical example is presented and discussed.Ministerio de Economía, Industria y Competitividad (Project CGL2008-06003-C03-03/CLI)Ministerio de Economía, Industria y Competitividad (Project ECO2012-32178)Ministerio de Economía, Industria y Competitividad (Project ECO2012-31933

A decade of application of the Choquet and Sugeno integrals in multi-criteria decision aid

The main advances regarding the use of the Choquet and Sugeno integrals in multi-criteria decision aid over the last decade are reviewed. They concern mainly a bipolar extension of both the Choquet integral and the Sugeno integral, interesting particular submodels, new learning techniques, a better interpretation of the models and a better use of the Choquet integral in multi-criteria decision aid. Parallel to these theoretical works, the Choquet integral has been applied to many new fields, and several softwares and libraries dedicated to this model have been developed.Choquet integral, Sugeno integral, capacity, bipolarity, preferences

Adjusted permutation method for multiple attribute decision making with meta-heuristic solution approaches

The permutation method of multiple attribute decision making has two significant deficiencies: high computational time and wrong priority output in some problem instances. In this paper, a novel permutation method called adjusted permutation method (APM) is proposed to compensate deficiencies of conventional permutation method. We propose Tabu search (TS) and particle swarm optimization (PSO) to find suitable solutions at a reasonable computational time for large problem instances. The proposed method is examined using some numerical examples to evaluate the performance of the proposed method. The preliminary results show that both approaches provide competent solutions in relatively reasonable amounts of time while TS performs better to solve APM

IT2-based fuzzy hybrid decision making approach to soft computing

WOS: 000459347800001This aims to evaluate the risk appetite of the financial investors in emerging economies using an integrated interval type-2 fuzzy model. For this purpose, eight different criteria are identified with the supporting literature. The interval type-2 fuzzy DEMAYEL approach is used to weight these criteria regarding the importance level. In addition, investors are classified into three different groups with respect to the risk appetite which are the aggressive/risk taker, moderate/risk neutral, and conservative/risk averse. Moreover, the interval type-2 fuzzy QUALIFLEX methodology is taken into consideration to rank these investor groups. The novelty of this paper is to propose a hybrid fuzzy decision-making approach to the investors' risk appetite based on the interval type-2 fuzzy sets. The findings show that aggressive investors play the most important role in emerging economies. Therefore, financial products, which offer high returns, should be developed to attract the attention of these aggressive investors. Owing to this aspect, it can be possible for emerging economies to improve their financial systems

Linguistic majorities with difference in support

The file attached to this record is the authors pre-print. The publishers final version of record can be found by following the DOI link

The Ordinal Input for Cardinal Output Approach of Non-compensatory Composite Indicators: The PROMETHEE Scoring Method

Despite serious threats as to their soundness, the adoption of composite indicators is constantly growing alongside their popularity, especially when it comes to their adoption in policy-making exercises. This study presents a robust non-compensatory approach to construct composite indicators mainly based, at least with respect to the basic ideas, on the classic Borda scoring procedure. The non- compensatory indicators we are proposing can be seen as aggregation of ordinal non-compensatory preferences between considered units supplying a numerical cardinal comprehensive evaluation. For this reason we define our methodology, the ordinal input for cardinal output non-compensatory approach for composite indicators. To take into account hesitation, imprecision and ill-determination in defining preference relations with respect to the elementary indices, we adopt the PROMETHEE methods, whose net flow score can be seen as an extension to the fuzzy preferences of the Borda score. Moreover, we systematically deal with robustness of the results with respect to weighting and parameters such as indifference and preference thresholds, permitting to define preference relations of elementary indices. In this regard, we couple PROMETHEE methods with the recently proposed σ−μ approach, which permits to explore the whole domain of feasible preference parameters mentioned above, giving a synthetic representation of the distribution of the values assumed by the composite indicators in terms of mean, μ, and standard deviation, σ. μ and σ are also used to define a comprehensive overall composite indicator. Finally, we enrich the results of this analysis with a set of graphical visualizations based on principal component analysis applied to the PROMETHEE methods with the GAIA technique, providing better understanding of the outcomes of our approach. To illustrate its assets, we provide a case study of inclusive development evaluation, based on the data of the homonymous report produced by the World Economic Forum

The Ordinal Input for Cardinal Output Approach of Non-compensatory Composite Indicators: The PROMETHEE Scoring Method

Despite serious threats as to their soundness, the adoption of composite indicators is constantly growing alongside their popularity, especially when it comes to their adoption in policy-making exercises. This study presents a robust non-compensatory approach to construct composite indicators mainly based, at least with respect to the basic ideas, on the classic Borda scoring procedure. The non- compensatory indicators we are proposing can be seen as aggregation of ordinal non-compensatory preferences between considered units supplying a numerical cardinal comprehensive evaluation. For this reason we define our methodology, the ordinal input for cardinal output non-compensatory approach for composite indicators. To take into account hesitation, imprecision and ill-determination in defining preference relations with respect to the elementary indices, we adopt the PROMETHEE methods, whose net flow score can be seen as an extension to the fuzzy preferences of the Borda score. Moreover, we systematically deal with robustness of the results with respect to weighting and parameters such as indifference and preference thresholds, permitting to define preference relations of elementary indices. In this regard, we couple PROMETHEE methods with the recently proposed σ−μ approach, which permits to explore the whole domain of feasible preference parameters mentioned above, giving a synthetic representation of the distribution of the values assumed by the composite indicators in terms of mean, μ, and standard deviation, σ. μ and σ are also used to define a comprehensive overall composite indicator. Finally, we enrich the results of this analysis with a set of graphical visualizations based on principal component analysis applied to the PROMETHEE methods with the GAIA technique, providing better understanding of the outcomes of our approach. To illustrate its assets, we provide a case study of inclusive development evaluation, based on the data of the homonymous report produced by the World Economic Forum

Semantic Similarity of Spatial Scenes

The formalization of similarity in spatial information systems can unleash their functionality and contribute technology not only useful, but also desirable by broad groups of users. As a paradigm for information retrieval, similarity supersedes tedious querying techniques and unveils novel ways for user-system interaction by naturally supporting modalities such as speech and sketching. As a tool within the scope of a broader objective, it can facilitate such diverse tasks as data integration, landmark determination, and prediction making. This potential motivated the development of several similarity models within the geospatial and computer science communities. Despite the merit of these studies, their cognitive plausibility can be limited due to neglect of well-established psychological principles about properties and behaviors of similarity. Moreover, such approaches are typically guided by experience, intuition, and observation, thereby often relying on more narrow perspectives or restrictive assumptions that produce inflexible and incompatible measures. This thesis consolidates such fragmentary efforts and integrates them along with novel formalisms into a scalable, comprehensive, and cognitively-sensitive framework for similarity queries in spatial information systems. Three conceptually different similarity queries at the levels of attributes, objects, and scenes are distinguished. An analysis of the relationship between similarity and change provides a unifying basis for the approach and a theoretical foundation for measures satisfying important similarity properties such as asymmetry and context dependence. The classification of attributes into categories with common structural and cognitive characteristics drives the implementation of a small core of generic functions, able to perform any type of attribute value assessment. Appropriate techniques combine such atomic assessments to compute similarities at the object level and to handle more complex inquiries with multiple constraints. These techniques, along with a solid graph-theoretical methodology adapted to the particularities of the geospatial domain, provide the foundation for reasoning about scene similarity queries. Provisions are made so that all methods comply with major psychological findings about people’s perceptions of similarity. An experimental evaluation supplies the main result of this thesis, which separates psychological findings with a major impact on the results from those that can be safely incorporated into the framework through computationally simpler alternatives